Researchers have developed an AI model that detects metastatic brain cancer spread with 85% accuracy using MRI scans, offering a non-invasive alternative to surgery and potential advancements in cancer treatment.
As AI adoption in breast cancer screening expands, RadNet’s Enhanced Breast Cancer Detection program tackles clinical and accessibility challenges. Here, RadNet’s Timothy M. Merchant and Karen Morad share insights on overcoming barriers, improving affordability, and validating AI’s role in care.
Google Cloud’s new report highlights how generative AI can reduce administrative burdens for radiologists by automating documentation and image analysis, improving efficiency, diagnostic accuracy, and patient care quality.
AI’s rapid growth in healthcare, particularly in radiology, has sparked environmental concerns, prompting the development of sustainable solutions like energy-efficient AI models, green computing, and renewable energy.
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National Institutes of Health researchers found that while an AI model accurately answered medical quiz questions, it often made errors in image descriptions and reasoning, highlighting both its potential and limitations in clinical settings.
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British researchers have developed an AI method for analyzing heart MRI scans that rapidly and accurately assesses heart function, potentially saving National Health Service time and resources while improving patient care through faster, non-invasive diagnostics.
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Children and young people are generally positive about AI in healthcare, especially in radiology, but emphasize the need for human supervision, according to a U.K.-based survey.
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AI tools can enhance medical imaging if radiologists trust their design, receive adequate training, and establish clear clinical accountability guidelines, with collaboration and regulatory support being essential for successful adoption, according to a report from the RSNA.
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